Shai: Enforcing Data-Specific Policies with Near-Zero Runtime Overhead
This addresses the challenge of policy compliance for systems like search engines and social networks, offering a systematic solution that is incremental in improving efficiency over existing methods.
The paper tackles the problem of enforcing data-specific policies in data retrieval systems with minimal runtime overhead, achieving near-zero overhead in common cases through a combination of offline analysis and selective runtime checks.
Data retrieval systems such as online search engines and online social networks must comply with the privacy policies of personal and selectively shared data items, regulatory policies regarding data retention and censorship, and the provider's own policies regarding data use. Enforcing these policies is difficult and error-prone. Systematic techniques to enforce policies are either limited to type-based policies that apply uniformly to all data of the same type, or incur significant runtime overhead. This paper presents Shai, the first system that systematically enforces data-specific policies with near-zero overhead in the common case. Shai's key idea is to push as many policy checks as possible to an offline, ahead-of-time analysis phase, often relying on predicted values of runtime parameters such as the state of access control lists or connected users' attributes. Runtime interception is used sparingly, only to verify these predictions and to make any remaining policy checks. Our prototype implementation relies on efficient, modern OS primitives for sandboxing and isolation. We present the design of Shai and quantify its overheads on an experimental data indexing and search pipeline based on the popular search engine Apache Lucene.